Data augmentation is classically used to improve the overall performance of deep learning models. It is, however, challenging in the case of medical applications, and in particular for multiparametric datasets. For example, traditional geometric transformations used in several applications to generate synthetic images can modify in a non-realistic manner the patients' anatomy. Therefore, dedicated image generation techniques are necessary in the medical field to, for example, mimic a given pathology realistically. This paper introduces a new data augmentation architecture that generates synthetic multiparametric (T1 arterial, T1 portal, and T2) magnetic resonance images (MRI) of massive macrotrabecular subtype hepatocellular carcinoma with their corresponding tumor masks through a generative deep learning approach. The proposed architecture creates liver tumor masks and abdominal edges used as input in a Pix2Pix network for synthetic data creation. The method's efficiency is demonstrated by training it on a limited multiparametric dataset of MRI triplets from $89$ patients with liver lesions to generate $1,000$ synthetic triplets and their corresponding liver tumor masks. The resulting Frechet Inception Distance score was $86.55$. The proposed approach was among the winners of the 2021 data augmentation challenge organized by the French Society of Radiology.

介绍了一种新的数据增广框架，通过生成式深度学习方法生成大规模宏小结肝细胞癌的多参数（T1动脉，T1门静脉和T2）磁共振图像及其相应的肿瘤掩膜，所提出的方法在有限的多参数MRI三元组数据集上训练，生成了1000个合成三元组及其相应的肝脏肿瘤掩膜，Frechet Inception Distance得分为86.55，此方法是由法国放射学学会组织的2021年数据增广挑战中的获胜者之一。

面具引导的多参数磁共振成像数据增强方法在一种罕见肝细胞癌中的应用